Everyone talks about AI in marketing, but what does it actually look like in the wild?
These examples show how real brands are using AI for content, personalization, customer care, listening, and campaign optimization.
Key takeaways
- AI works best when it has a specific job. Use it to solve a real problem, like scaling content, personalizing creative, or speeding up customer care.
- The results still need a reality check. AI doesn’t automatically outperform human work, so testing and iteration still matter.
- Hootsuite puts AI into the work you’re already doing. Wisdom turns social data into next steps, Perch helps create on-brand content, and Lumen surfaces trends and sentiment before they’re old news.
How is AI actually being used in marketing today?
Marketing teams are using AI to create content, personalize messaging, make sense of social listening data, handle customer support, and figure out what to do next.
Here are some of the biggest ways it shows up:
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Content creation
80% of marketers now use AI for content creation. It’s a common entry point, mostly because it’s the easiest to try. You give it a prompt, it gives you a draft, and you edit from there.
Social marketers can use AI to:
- Draft captions and copy
- Brainstorm hooks, angles, and content ideas
- Repurpose one piece of content into versions for different platforms
- Rewrite copy for a different tone or audience
Pro tip 💡: If you’re working with AI-powered tools for content creation, you need a really good workflow. It’s how you stop an off-brand post from slipping through the cracks.
Personalization
In 2026, personalization is the norm. Open any social media app, and you expect the algorithm to know what you’re into. When it gets it wrong, it’s painfully obvious.
That raises the bar for marketers. People want brands to meet them with the same precision their feed does.
AI makes that kind of personalization easier to pull off at scale. Social teams can use it to:
- Adjust messaging and ad copy for different audience segments
- Personalize offers, timing, or calls to action
- Spot patterns in audience behavior that would be hard to catch manually
Social listening & sentiment analysis
The challenge with social listening has always been volume. No team can manually scroll through thousands of mentions and come away with a clear picture.
That’s where AI helps. It can scan large volumes of conversation and pull out the things a human team would struggle to catch at a glance, like:
- Changes in sentiment around your brand
- Recurring complaints or questions
- New topics or phrases picking up steam
- A sudden spike in mentions
- Differences in how people talk about you versus competitors
And sentiment analysis adds another layer. It helps sort the tone of those conversations, so you can see whether mentions are broadly positive, negative, or somewhere in the middle.
Customer care
AI can show up in customer care a few different ways:
- Customer-facing chatbots: These talk directly to customers on your website or app. They can answer FAQs, help with returns, or check an order status.
- AI agents: Agents are a step beyond a basic chatbot. They can sometimes take actions, like updating an order or escalating an issue.
- Routing and triage: AI can read incoming messages and decide where they should go. A billing problem goes to billing and an angry customer goes to a human representative.
- Conversation analysis: AI can look across thousands of support chats, DMs, or comments and surface patterns.
The biggest wins here are speed and coverage. AI keeps simple questions moving around the clock, and it helps human reps get through the harder conversations faster, too.
For example, researchers at Harvard Business School analyzed a year’s worth of online chat conversations between a meal delivery company and its customers. They found that AI helped human agents respond to chats about 20% faster.
Campaign performance and optimization
Most campaign reports are a wall of numbers. Impressions up, conversion rate down, engagement flat. Now what?
AI scans performance data and surfaces the patterns actually worth paying attention to. It can:
- Spot which posts, formats, or messages are driving results
- Flag underperforming content before you waste more budget on it
- Compare performance across audiences or channels
- Summarize campaign results
One of the best parts is that teams can make adjustments while a campaign is still running, instead of learning what went wrong after the fact.
What are the best examples of AI in marketing?
Unilever uses AI to scale content, Wembley Stadium uses it to automate customer care, Reebok uses it to analyze social conversations, Kraft uses it to sharpen influencer selection, and Popeyes UK uses it to optimize ad campaigns.
Let’s dive in:
Content creation and copywriting: Unilever (AXE and Degree)
Creating one good piece of content is one thing. Creating 162 of them without completely overwhelming your team is another.
Unilever did exactly that for two of its deodorant brands, AXE and Degree. The team built a proprietary AI tool trained on each brand’s voice, then used it to create 162 pages of educational content around topics like “why does sweat smell like onions?” (Yes, really).
The content went beyond articles too, with quizzes, infographics, video, and revamped FAQs.
The extra output paid off: Degree now holds 37% share of voice in AI Overviews for the US deodorant category, while content production moved 3x faster.
Key takeaway: AI can help you scale content without flattening every brand into the same voice. The trick is giving it a strong voice to work from in the first place.
What to steal:
- Start with the questions your audience is already asking, then use AI to build useful answers around them.
- Train your AI on approved brand examples.
- Use AI to turn one strong idea into multiple formats, rather than cranking out five versions of the same article.
Personalization at scale: Headspace
“Holiday stress” means different things to different people. For a college student, it’s final exams. For someone else, it’s an overbooked calendar.
Headspace used AI to turn all those different stressors into different versions of the same campaign. The team created hundreds of assets across 20 use cases, then used Meta’s Advantage+ to match each variation with the people most likely to relate to it.
Source: Adweek
In total, Headspace produced 460 assets in under two weeks, cutting production time by 67% and increasing app sign-ups by 13%.
Key takeaway: Personalization gets a lot more useful when the creative itself changes, not just the audience targeting.
What to steal:
- Look for meaningful differences within your audience, then build content around them.
- Use AI to create variations of one strong campaign idea without rebuilding every asset from scratch.
AI-assisted customer care: Wembley Stadium
Wembley Stadium can get up to 8,000 customer inquiries a day. That’s enough to burn out any support team.
To take the pressure off, Wembley added an AI chatbot to its website. It’s trained on event-specific details, which means fans can get quick answers instead of sitting in a queue.
The bot now handles about 12,000 chats a month. It also helps with lead generation, qualifying people interested in premium memberships before handing them over to a salesperson.
Key takeaway: AI can handle the repetitive questions first, so humans have more time for the ones that actually need their attention.
What to steal:
- Start with the questions your team answers over and over again. Those are usually the easiest to hand off to AI.
- Train your chatbot on timely information, not just a static FAQ page.
- Give people a clear path to a human when the conversation gets more complicated.
Influencer marketing: Kraft
When Kraft launched its plant-based line, it had two audiences in mind: people looking for plant-based options, and people who just want familiar comfort food.
Instead of lumping them together, Kraft used AI-powered audience segmentation to tease those groups apart, then find creators who made sense for each one.
Source: BENlabs
The campaign landed 15 creators across 26 pieces of content, and pulled in over 2.4 million views.
Key takeaway: AI can help you get more specific about which creators make sense for different audiences.
What to steal:
- Use AI-powered audience segmentation to find the meaningful differences within your audience
- Use audience data to choose creators, not follower count alone.
Social listening and sentiment analysis: Reebok
Reebok wanted to know how people actually talked about them in the CrossFit world, and how that compared with its competitors.
So Novicell, a digital marketing agency, set up Lumen by Talkwalker to track conversations across social networks and other online channels. The team analyzed more than 14,000 conversations from nearly 5,000 users.
They found more than 25 opportunities to improve, including ways to sharpen Reebok’s positioning and spot new business opportunities.
Key takeaway: Social listening gets much more useful when you can turn thousands of scattered conversations into a few clear action items.
What to steal:
- Track your category, not just your brand name.
- Compare how people talk about you with how they talk about competitors.
- Use listening data to find the recurring themes that should actually change your content or strategy.
Campaign optimization: Popeyes UK
Popeyes UK didn’t need help making ads. It needed help getting them in front of the right people.
So the brand used AI to help decide who should see the ads and how much to bid for each placement. As the campaign ran, the AI adjusted based on what was actually performing instead of sticking with the same setup from start to finish.
That optimization added up to 22 million impressions, 45,000 conversions, and a 678% increase in ROAS.
Key takeaway: AI can optimize a campaign while it’s still running.
What to steal:
- Use AI to refine who sees your ads, not just to make more ads.
- Let real performance data shape targeting and bidding as the campaign runs.
What are the risks of using AI in marketing?
AI can make marketing faster, but it can also make the work more predictable, generic, and less human. We asked Maria LaMagna Morales, Founder of Press Publish Studio, what marketers should watch out for:
1. Losing the weirdness that makes marketing interesting
AI is predictable. Literally. It’s trained to produce the most reasonable next word, next sentence, next idea.
Which is a problem, because “weird” is often the thing that gets people’s attention.
“In social media now, it’s absolutely critical to hook your viewer, to stop them from continuing to scroll. And with the rise of TikTok and Reels, audiences are primed to keep moving on if their interest isn’t piqued right away,” says Morales.
Sometimes, that means getting a little weird. She points to creators who deliberately do something odd, like holding an unexpected prop or clipping a microphone somewhere strange. That randomness is the point, and it’s hard for AI to produce.
AI isn’t designed to suggest that strangeness, or surprise and delight. You risk too much polish, and not enough ‘Wait, what was that?’
2. Polishing away your brand voice
Every time you ask AI to make something “more concise” or “more professional,” you take a tiny step away from your brand voice. The first pass may clean things up. By the third, it’s sanded off everything that gave your writing character.
Morales’ advice is simple: Write the way you actually speak.
“In conversation, we naturally use slang, swear, go on tangents, and throw in extra words like ‘really,’ ‘definitely,’ and ‘very.’ That’s okay! It’s better to make something sound human than to make it sound streamlined,” she says.
3. Over-automating the creative process
Even as AI gets more popular, people still gravitate toward things that feel unmistakably human.
Take AI-generated imagery, for example. Morales sees it as one of the more overhyped uses of the technology.
“At first, creating digital imagery with AI was really exciting and fun. Now, we’re more used to seeing it, and in many cases audiences don’t want it,” she says. “I think we’re seeing a desire for MORE real photography, texture, human faces, and images that reflect our day-to-day lives.”
This isn’t an argument against generative imagery, or AI in general. It’s about being more deliberate about when to use it.
If AI can generate an impressive image in seconds, “impressive” starts to mean a little less. What feels valuable is the stuff that can’t be fabricated so easily, like a photo of an actual employee or a real customer using your product.
How does Hootsuite bring AI into everyday marketing work?
Hootsuite brings AI into the same place you plan content, check performance, and monitor mentions.
Ask questions in plain language with Wisdom
Wisdom is Hootsuite’s social-first AI agent. Ask it questions like “What’s shaping perception of us this week?” or “Which posts performed best last month?” and get an answer grounded in your actual social data.
It doesn’t just stop at the answer. Wisdom can also recommend what to do next, then turn that insight into your next post or campaign idea.
Generate on-brand content with Perch
Perch is where the content work actually happens. Teams can create, edit, approve, schedule, and publish posts, all in one place.
With Wisdom built in, you can brainstorm ideas, draft captions, tailor posts for different channels, adjust the tone, and keep content moving without losing your brand voice along the way.
Spot trends and sentiment with Lumen
Lumen is Hootsuite’s listening layer. It tracks brand mentions, sentiment, and emerging trends across more than 150 million sources, so you can catch a shift in how people are talking about your brand before it becomes a bigger problem (or a bigger opportunity).
Together, Lumen and Wisdom help teams move from listening to action faster. Lumen surfaces the signals, and Wisdom explains what’s happening and recommends next steps — whether that’s adjusting your messaging, creating timely content, or getting ahead of a potential crisis.
FAQ: AI in marketing
What is AI in marketing?
It includes generative AI tools that draft copy and images (like ChatGPT or Claude), chatbots that talk to customers directly, predictive analytics that forecast things like consumer behavior, and recommendation engines that personalize what people see (think of how Netflix knows exactly what you like to watch). AI algorithms also quietly power many marketing automation platforms.
What’s a simple example of AI in marketing?
Is AI replacing marketers?
How does AI help with marketing content creation?
How does Hootsuite bring AI into everyday marketing work?
Bring AI into the marketing work you’re already doing with Hootsuite. Ask Wisdom to turn social data into answers and next steps, use Perch to create and schedule on-brand content, and rely on Lumen to spot trends and sentiment shifts before they pass you by. Try Hootsuite free today.
